The amount of images captured by users has increased exponentially due to the increase in image capture device availability, e.g., cameras as part of mobile phones and tablets. Even a casual user may capture hundreds of images in a variety of different settings, such as indoors (e.g., at a user's home) as well as outdoors (e.g., a kids sporting event).
As these images are typically captured in a casual manner, however, the images may include distractors, which are objects included in the image that may distract a user from primary content in the image that is the subject of the image. For example, an image captured of a couple in a beach setting may also include other objects that are not relevant to the couple or the beach setting, such as a dog running in the background, a waste receptacle, and so forth. Thus, the dog and the receptacle may distract from the primary content of the image, i.e., the couple at the beach in this example. Conventional techniques used to remove distractors from images, however, typically required users to manually indicate which objects are considered distracting as part of an object removal process and are not able to be generalized to support automatic detection and removal.